A Contactless Visitor Access Monitoring System

Abstract

This project presents a contactless visitor access monitoring in small premises which implemented deep learning model in face recognition, develop the graphical user interface (GUI) for new visitor registration and visitor identification. Five stages of monitoring process are designed in the contactless visitor access monitoring (CVAM) GUI, the first step is to give instructions to the admin user regarding the monitoring process, the second step is to perform face recognition, the third step is to scan the body temperature, the fourth step is to perform mask detection on the visitor, and the final stage is to record visitor access time. Another visitor registration (VisReg) GUI is designed to register new visitors into the system. In VisReg, admin user is required to pre-process face images with MTCNN technique and generate new classifier with a ResNet pre-trained model. The contactless visitor access monitoring process is demonstrated. The face recognition gives an accuracy of 82%, while the mask detection gives an accuracy of 95% when tested with the validation dataset. It can be concluded that the visitor monitoring process can be carried out in a contactless way to eliminate the close contact between the security officers, receptionist, and visitors.